Two theoretical and computational tasks are of central importance in structure-based drug design: (1) prediction of protein/ligand conformations and binding free-energies, and (2) computational refinement of X-ray diffraction and NMR macromolecular structure data. Our goal is to develop and test the practical utility of three new methods for addressing these problems. We will use stochastic analysis of interatomic motions to map flexible and rigid protein subregions and to provide a basis for interactive protein-ligand modeling software that allows for flexible deformation. The "time-bin" algorithm uses stochastic analysis to increase the efficiency ad accuracy of molecular dynamics simulations of protein-ligand-solvent systems. This should permit longer simulations with larger proteins and more solvent molecules. "Packet-annealing" is a novel global minimization method for identifying the lowest free-energy conformations of protein-ligand- solvent systems. It replaces the stochastic sampling of simulated annealing and Monte Carlo methods by more efficient deterministic renormalization group analysis with effective potentials. Partial implementation and testing has been completed to demonstrate feasibility. We will develop, implement and test the capabilities of these methods on a variety of practical problems including improved NMR and X-ray diffraction structural data refinement and evaluation of protein-ligand binding conformations and free-energies. We will also use stochastic propagator methods to evaluate errors in MD simulations. These studies are part of our long-term goal to improve th theoretical and computational techniques applicable to structure-based drug design.